{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:02:10Z","timestamp":1781222530413,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698653","type":"print"},{"value":"9789819698660","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-9866-0_4","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T09:24:32Z","timestamp":1753262672000},"page":"40-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mitigating Spurious Correlations in Few-Shot Classification via Bias and Dynamic Prompt"],"prefix":"10.1007","author":[{"given":"Yalong","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuiyi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"4_CR1","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, vol. 139, pp. 8748\u20138763 (2021)"},{"key":"4_CR2","first-page":"1","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural. Inf. Process. Syst. 30, 1\u201311 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4_CR3","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"3","key":"4_CR4","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s11263-023-01916-5","volume":"132","author":"SS Ghosal","year":"2024","unstructured":"Ghosal, S.S., Li, Y.: Are vision transformers robust to spurious correlations? Int. J. Comput. Vis. 132(3), 689\u2013709 (2024)","journal-title":"Int. J. Comput. Vis."},{"issue":"9","key":"4_CR5","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vis. 130(9), 2337\u20132348 (2022)","journal-title":"Int. J. Comput. Vis."},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Jia, M., et al.: Visual prompt tuning. In: European Conference on Computer Vision, pp. 709\u2013727. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15211\u201315222 (2023)","DOI":"10.1109\/CVPR52729.2023.01460"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: AMU-Tuning: effective logit bias for CLIP-based few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23323\u201323333 (2024)","DOI":"10.1109\/CVPR52733.2024.02201"},{"issue":"2","key":"4_CR9","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s11263-023-01891-x","volume":"132","author":"P Gao","year":"2024","unstructured":"Gao, P., et al.: CLIP-Adapter: better vision-language models with feature adapters. Int. J. Comput. Vis. 132(2), 581\u2013595 (2024)","journal-title":"Int. J. Comput. Vis."},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Sung, F., et al.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 6th Indian Conference on Computer Vision, Graphics & Image Processing, India, pp. 722\u2013729. IEEE (2008)","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"4_CR12","first-page":"14274","volume":"35","author":"M Shu","year":"2022","unstructured":"Shu, M., et al.: Test-time prompt tuning for zero-shot generalization in vision-language models. Adv. Neural. Inf. Process. Syst. 35, 14274\u201314289 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: Proc. IEEE\/CVF International Conference on Computer Vision, pp. 9640\u20139649 (2021)","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"4_CR14","unstructured":"Zhang, R., et al.: Tip-Adapter: training-free CLIP-Adapter for better vision-language modeling. arXiv preprint arXiv:2111.03930 (2021)"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Yao, H., Zhang, R., Xu, C.: Visual-language prompt tuning with knowledge-guided context optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6757\u20136767 (2023)","DOI":"10.1109\/CVPR52729.2023.00653"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Yu, T., et al.: Task residual for tuning vision-language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10899\u201310909 (2023)","DOI":"10.1109\/CVPR52729.2023.01049"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Khattak, M.U., et al.: MAPLE: multi-modal prompt learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19113\u201319122 (2023)","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, B., et al.: Prompt-aligned gradient for prompt tuning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15659\u201315669 (2023)","DOI":"10.1109\/ICCV51070.2023.01435"},{"key":"4_CR20","unstructured":"Zhou, Y. et al.: Analyzing and mitigating object hallucination in large vision-language models. arXiv preprint arXiv:2310.00754 (2023)"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Gao, J. et al.: LAMM: label alignment for multi-modal prompt learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 3, pp. 1815\u20131823 (2024)","DOI":"10.1609\/aaai.v38i3.27950"},{"key":"4_CR22","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4_CR23","unstructured":"Zhai, X. et al.: A large-scale study of representation learning with the visual task adaptation benchmark. arXiv preprint arXiv:1910.04867 (2019)"},{"key":"4_CR24","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9866-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T23:04:17Z","timestamp":1781219057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9866-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698653","9789819698660"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9866-0_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}